A tactical guide for Google Gemini 3.1 Pro

The quick answer

Google has released Gemini 3.1 Pro, an upgraded AI model with improved reasoning skills. Here is how you can put it to work:

1. Understand its core improvements: Gemini 3.1 Pro focuses on logical reasoning and problem-solving, making it better at complex, multi-step tasks.
2. Know its new capabilities: The model can generate code for websites, create animated SVGs from text, and independently configure data streams for dashboards.
3. Find where to access it: You can access the preview through the Gemini API, Google AI Studio, Vertex AI, the Gemini app, and NotebookLM for subscribers.
4. Test it on your own projects: The best way to see the improvements is to use your own specific prompts and compare its output to previous models.

What is Google Gemini 3.1 Pro?

Google has released Gemini 3.1 Pro, a significant update to its AI model family. This new version is not just an incremental tweak. It is built to improve the model's core intelligence, especially in logical reasoning and problem-solving.

The update aims to bring the advanced thinking seen in specialized models like Gemini 3 Deep Think to everyday use. This means you can use its enhanced intelligence for common business and development tasks, not just complex scientific research.

Closing the gap between power and use

A key goal of Gemini 3.1 Pro is to make powerful AI more user-friendly. The model is designed to handle complex API interactions and coding tasks behind the scenes, delivering a simple result based on your natural language prompt.

This allows you to focus on the outcome you want, not the technical steps required to get there. It's a move toward more practical, results-driven AI applications.

What Gemini 3.1 Pro can actually do

Benchmarks provide numbers, but real-world examples show what an AI can deliver. Google has demonstrated several practical use cases for Gemini 3.1 Pro that highlight its improved reasoning and agentic capabilities.

These examples show the model handling tasks that require multiple steps, context, and code generation.

Example 1: Build a live data dashboard

In one demonstration, Gemini 3.1 Pro independently configured a public telemetry stream to visualize the orbit of the International Space Station. The model located the correct data source, processed it, and created a live aerospace dashboard without manual coding from a developer.

This shows its ability to work with real-time data and build functional tools from a high-level request.

Example 2: Generate web assets from text

The model can also create animated Scalable Vector Graphics (SVGs) directly from a text prompt. You can describe an animation, and the model generates the necessary code to embed it directly on a website.

It can also spin up entire websites from scratch. These capabilities speed up development cycles and reduce the need for specialized design software or extensive coding for certain assets. Building websites that convert requires a mix of great design and smart functionality, and tools like this can accelerate the process.

The benchmark breakdown

While real-world tests are key, benchmarks offer a standardized way to measure performance improvements. According to the original article from the-decoder.com, Gemini 3.1 Pro shows significant gains over its predecessor and competitors.

Keep in mind that high benchmark scores do not always translate to a perfect user experience, but they do indicate a jump in the model's underlying capabilities.

Dramatic improvement in abstract reasoning

The most notable result comes from the ARC-AGI-2 benchmark, which tests abstract logic tasks. Here are the scores reported by Google:

• Gemini 3.1 Pro: 77.1%
• Gemini 3 Pro (previous model): 31.1%
• Anthropic Opus 4.6: 68.8%
• OpenAI GPT-5.2: 52.9%

The score for Gemini 3.1 Pro is more than double its predecessor, showing a major leap in its ability to solve novel, abstract problems.

Strong performance across other key areas

Gemini 3.1 Pro also performs well on other important benchmarks, demonstrating its versatility. Here are some of the scores Google provided:

• GPQA Diamond (Scientific Knowledge): 94.3%
• BrowseComp (Agentic Web Browsing): 85.9%
• MCP Atlas (Multi-step Agentic Tasks): 69.2%
• SWE-Bench Verified (Agentic Coding): 80.6%
• MMMU Pro (Multimodal Reasoning): 80.5%
• LiveCodeBench Pro (Competitive Coding): Elo score of 2,887

These scores suggest the model is highly capable in tasks involving web navigation, coding, and applying scientific knowledge. This makes it a powerful tool for developing content and automated systems that require deep understanding. By leveraging such tools, you can enhance your SEO content strategy with more sophisticated and data-driven assets.

How you can access Gemini 3.1 Pro

Google is rolling out the preview of Gemini 3.1 Pro across its suite of AI products. Access is designed for different types of users, from individual developers to large enterprises.

You can start experimenting with the model's new features on several platforms right now.

For developers

If you are a developer, you can access Gemini 3.1 Pro through these channels:

• Gemini API and Google AI Studio: The most direct way to build applications with the new model.
• Gemini CLI: For command-line access and scripting.
• Google Antigravity: An agent-based development platform for building complex AI workflows.
• Android Studio: For integrating the model into mobile applications.

For businesses and enterprises

Companies can use the model in a managed, enterprise-grade environment. Access is available through:

• Vertex AI: Google's unified AI platform for building, deploying, and scaling models.
• Gemini Enterprise: A dedicated offering for businesses looking for security, governance, and support.

For end users

If you want to try the model for personal or professional productivity, you can find it in:

• The Gemini app: The consumer-facing application for direct interaction with the model.
• NotebookLM: Available for Pro and Ultra subscribers, this tool uses Gemini to help you understand and synthesize your own documents.

The pricing structure explained

Gemini 3.1 Pro is a premium model, and its preview comes with a tiered pricing structure based on token usage. A "token" is a small piece of a word, roughly equal to 4 characters.

Here is the pricing breakdown for API usage:

Category	Up to 200,000 tokens	Over 200,000 tokens
Input	$2.00 / 1M tokens	$4.00 / 1M tokens
Output	$12.00 / 1M tokens	$18.00 / 1M tokens
Caching	$0.20 / 1M tokens	$0.40 / 1M tokens
Cache storage	$4.50 / 1M tokens per hour	$4.50 / 1M tokens per hour
Search	5,000 prompts/month free, then $14.00 / 1,000 queries

This tiered model encourages more efficient use of the AI, as longer and more complex interactions cost more. The model is still in preview, so Google will continue to adjust it based on feedback before a general availability release.

How to test Gemini 3.1 Pro effectively

The best way to understand the model's improvements is to test it yourself. Simply asking it random questions is not enough. Follow a structured process to get a clear picture of its capabilities.

Step 1: Reuse old prompts

Start with prompts you have used on other models like Gemini 3 Pro, GPT-4, or Claude 3. Use tasks where you know what good output looks like and where previous models struggled.

This direct comparison makes it easy to spot improvements in quality, detail, or reasoning.

Step 2: Focus on its strengths

Test the model on tasks it was built for. Give it prompts that require multi-step reasoning, coding, or data analysis. For example:

• "Write a Python script that pulls the top 5 questions from the Stack Overflow API for the tag 'javascript' and saves them to a CSV file."
• "Analyze this customer feedback and identify the 3 main themes. For each theme, suggest one actionable improvement."
• "Given this set of sales data, create the code for a bar chart that shows sales per region, with a trendline."

Step 3: Test agentic workflows

An "agentic workflow" is where the AI completes a goal by executing a series of actions on its own. Give Gemini 3.1 Pro a high-level goal that requires multiple steps to see how it performs.

For example, ask it to plan a marketing campaign for a fictional product. A good response would involve defining an audience, suggesting channels, writing sample copy, and outlining a budget. This tests its ability to think strategically and organize complex information. As you can see from our portfolio, successful digital marketing a result of clear, strategic steps, which is exactly what agentic AI aims to replicate.